Along with the development of Internet technology, search engines such as Bing®, Google® and Yahoo® currently provide text-based image search services to internet users. These image search services allow a user entering a keyword to describe an image the user wants to find, and retrieve one or more database images based on the entered keyword. In order to retrieve the desired image however, the entered keyword needs to describe the image accurately and/or sufficiently. Furthermore, this type of image search requires a database image to have one or more textual annotations in order to allow comparison and retrieval of that particular database image. Given that millions of images exist on the Internet, this unavoidably places a tremendous workload on the search engines. Moreover, the images must be accurately and completely tagged with the textual annotations in order for the images to be discovered using a text search query.
In view of the deficiencies of the text-based image search, some search engines now provide content-based image retrieval (CBIR) services. A user submits a query image to the search engine, which then analyzes the actual contents (e.g., colors, shapes and textures) of the query image. Based on a result of the analysis, the search engine retrieves images that are similar to or related to the query image. However, this type of content-based image retrieval is still in an immature stage. Research is actively conducted to determine effective and accurate image search and retrieval strategies and/or algorithms. In addition, the current state-of-the-art content-based image retrieval method is data-centric rather than user-centric. For example, existing image retrieval systems do not consider users' bias.